/*
 * SPDX-License-Identifier: Apache-2.0
 *
 * The OpenSearch Contributors require contributions made to
 * this file be licensed under the Apache-2.0 license or a
 * compatible open source license.
 */

/*
 * Licensed to Elasticsearch under one or more contributor
 * license agreements. See the NOTICE file distributed with
 * this work for additional information regarding copyright
 * ownership. Elasticsearch licenses this file to you under
 * the Apache License, Version 2.0 (the "License"); you may
 * not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 * KIND, either express or implied.  See the License for the
 * specific language governing permissions and limitations
 * under the License.
 */
/*
 * Modifications Copyright OpenSearch Contributors. See
 * GitHub history for details.
 */

package org.opensearch.search.aggregations.metrics;

import org.apache.lucene.index.LeafReaderContext;
import org.apache.lucene.search.ScoreMode;
import org.opensearch.common.lease.Releasables;
import org.opensearch.common.util.BigArrays;
import org.opensearch.common.util.DoubleArray;
import org.opensearch.common.util.LongArray;
import org.opensearch.core.ParseField;
import org.opensearch.index.fielddata.SortedNumericDoubleValues;
import org.opensearch.search.DocValueFormat;
import org.opensearch.search.aggregations.Aggregator;
import org.opensearch.search.aggregations.InternalAggregation;
import org.opensearch.search.aggregations.LeafBucketCollector;
import org.opensearch.search.aggregations.LeafBucketCollectorBase;
import org.opensearch.search.aggregations.support.ValuesSource;
import org.opensearch.search.aggregations.support.ValuesSourceConfig;
import org.opensearch.search.internal.SearchContext;

import java.io.IOException;
import java.util.Map;

/**
 * Aggregate all docs into their extended statistics
 *
 * @opensearch.internal
 */
class ExtendedStatsAggregator extends NumericMetricsAggregator.MultiValue {

    static final ParseField SIGMA_FIELD = new ParseField("sigma");

    final ValuesSource.Numeric valuesSource;
    final DocValueFormat format;
    final double sigma;

    LongArray counts;
    DoubleArray sums;
    DoubleArray compensations;
    DoubleArray mins;
    DoubleArray maxes;
    DoubleArray sumOfSqrs;
    DoubleArray compensationOfSqrs;

    ExtendedStatsAggregator(
        String name,
        ValuesSourceConfig valuesSourceConfig,
        SearchContext context,
        Aggregator parent,
        double sigma,
        Map<String, Object> metadata
    ) throws IOException {
        super(name, context, parent, metadata);
        // TODO: stop depending on nulls here
        this.valuesSource = valuesSourceConfig.hasValues() ? (ValuesSource.Numeric) valuesSourceConfig.getValuesSource() : null;
        this.format = valuesSourceConfig.format();
        this.sigma = sigma;
        if (valuesSource != null) {
            final BigArrays bigArrays = context.bigArrays();
            counts = bigArrays.newLongArray(1, true);
            sums = bigArrays.newDoubleArray(1, true);
            compensations = bigArrays.newDoubleArray(1, true);
            mins = bigArrays.newDoubleArray(1, false);
            mins.fill(0, mins.size(), Double.POSITIVE_INFINITY);
            maxes = bigArrays.newDoubleArray(1, false);
            maxes.fill(0, maxes.size(), Double.NEGATIVE_INFINITY);
            sumOfSqrs = bigArrays.newDoubleArray(1, true);
            compensationOfSqrs = bigArrays.newDoubleArray(1, true);
        }
    }

    @Override
    public ScoreMode scoreMode() {
        return valuesSource != null && valuesSource.needsScores() ? ScoreMode.COMPLETE : ScoreMode.COMPLETE_NO_SCORES;
    }

    @Override
    public LeafBucketCollector getLeafCollector(LeafReaderContext ctx, final LeafBucketCollector sub) throws IOException {
        if (valuesSource == null) {
            return LeafBucketCollector.NO_OP_COLLECTOR;
        }
        final BigArrays bigArrays = context.bigArrays();
        final SortedNumericDoubleValues values = valuesSource.doubleValues(ctx);
        final CompensatedSum compensatedSum = new CompensatedSum(0, 0);
        final CompensatedSum compensatedSumOfSqr = new CompensatedSum(0, 0);
        return new LeafBucketCollectorBase(sub, values) {

            @Override
            public void collect(int doc, long bucket) throws IOException {
                if (bucket >= counts.size()) {
                    final long from = counts.size();
                    final long overSize = BigArrays.overSize(bucket + 1);
                    counts = bigArrays.resize(counts, overSize);
                    sums = bigArrays.resize(sums, overSize);
                    compensations = bigArrays.resize(compensations, overSize);
                    mins = bigArrays.resize(mins, overSize);
                    maxes = bigArrays.resize(maxes, overSize);
                    sumOfSqrs = bigArrays.resize(sumOfSqrs, overSize);
                    compensationOfSqrs = bigArrays.resize(compensationOfSqrs, overSize);
                    mins.fill(from, overSize, Double.POSITIVE_INFINITY);
                    maxes.fill(from, overSize, Double.NEGATIVE_INFINITY);
                }

                if (values.advanceExact(doc)) {
                    final int valuesCount = values.docValueCount();
                    counts.increment(bucket, valuesCount);
                    double min = mins.get(bucket);
                    double max = maxes.get(bucket);
                    // Compute the sum and sum of squires for double values with Kahan summation algorithm
                    // which is more accurate than naive summation.
                    double sum = sums.get(bucket);
                    double compensation = compensations.get(bucket);
                    compensatedSum.reset(sum, compensation);

                    double sumOfSqr = sumOfSqrs.get(bucket);
                    double compensationOfSqr = compensationOfSqrs.get(bucket);
                    compensatedSumOfSqr.reset(sumOfSqr, compensationOfSqr);

                    for (int i = 0; i < valuesCount; i++) {
                        double value = values.nextValue();
                        compensatedSum.add(value);
                        compensatedSumOfSqr.add(value * value);
                        min = Math.min(min, value);
                        max = Math.max(max, value);
                    }

                    sums.set(bucket, compensatedSum.value());
                    compensations.set(bucket, compensatedSum.delta());
                    sumOfSqrs.set(bucket, compensatedSumOfSqr.value());
                    compensationOfSqrs.set(bucket, compensatedSumOfSqr.delta());
                    mins.set(bucket, min);
                    maxes.set(bucket, max);
                }
            }

        };
    }

    @Override
    public boolean hasMetric(String name) {
        try {
            InternalExtendedStats.Metrics.resolve(name);
            return true;
        } catch (IllegalArgumentException iae) {
            return false;
        }
    }

    @Override
    public double metric(String name, long owningBucketOrd) {
        if (valuesSource == null || owningBucketOrd >= counts.size()) {
            switch (InternalExtendedStats.Metrics.resolve(name)) {
                case count:
                    return 0;
                case sum:
                    return 0;
                case min:
                    return Double.POSITIVE_INFINITY;
                case max:
                    return Double.NEGATIVE_INFINITY;
                case avg:
                    return Double.NaN;
                case sum_of_squares:
                    return 0;
                case variance:
                    return Double.NaN;
                case variance_population:
                    return Double.NaN;
                case variance_sampling:
                    return Double.NaN;
                case std_deviation:
                    return Double.NaN;
                case std_deviation_population:
                    return Double.NaN;
                case std_deviation_sampling:
                    return Double.NaN;
                case std_upper:
                    return Double.NaN;
                case std_lower:
                    return Double.NaN;
                default:
                    throw new IllegalArgumentException("Unknown value [" + name + "] in common stats aggregation");
            }
        }
        switch (InternalExtendedStats.Metrics.resolve(name)) {
            case count:
                return counts.get(owningBucketOrd);
            case sum:
                return sums.get(owningBucketOrd);
            case min:
                return mins.get(owningBucketOrd);
            case max:
                return maxes.get(owningBucketOrd);
            case avg:
                return sums.get(owningBucketOrd) / counts.get(owningBucketOrd);
            case sum_of_squares:
                return sumOfSqrs.get(owningBucketOrd);
            case variance:
                return variance(owningBucketOrd);
            case variance_population:
                return variancePopulation(owningBucketOrd);
            case variance_sampling:
                return varianceSampling(owningBucketOrd);
            case std_deviation:
                return Math.sqrt(variance(owningBucketOrd));
            case std_deviation_population:
                return Math.sqrt(variance(owningBucketOrd));
            case std_deviation_sampling:
                return Math.sqrt(varianceSampling(owningBucketOrd));
            case std_upper:
                return (sums.get(owningBucketOrd) / counts.get(owningBucketOrd)) + (Math.sqrt(variance(owningBucketOrd)) * this.sigma);
            case std_lower:
                return (sums.get(owningBucketOrd) / counts.get(owningBucketOrd)) - (Math.sqrt(variance(owningBucketOrd)) * this.sigma);
            default:
                throw new IllegalArgumentException("Unknown value [" + name + "] in common stats aggregation");
        }
    }

    private double variance(long owningBucketOrd) {
        return variancePopulation(owningBucketOrd);
    }

    private double variancePopulation(long owningBucketOrd) {
        double sum = sums.get(owningBucketOrd);
        long count = counts.get(owningBucketOrd);
        double variance = (sumOfSqrs.get(owningBucketOrd) - ((sum * sum) / count)) / count;
        return variance < 0 ? 0 : variance;
    }

    private double varianceSampling(long owningBucketOrd) {
        double sum = sums.get(owningBucketOrd);
        long count = counts.get(owningBucketOrd);
        double variance = (sumOfSqrs.get(owningBucketOrd) - ((sum * sum) / count)) / (count - 1);
        return variance < 0 ? 0 : variance;
    }

    @Override
    public InternalAggregation buildAggregation(long bucket) {
        if (valuesSource == null || bucket >= counts.size()) {
            return buildEmptyAggregation();
        }
        return new InternalExtendedStats(
            name,
            counts.get(bucket),
            sums.get(bucket),
            mins.get(bucket),
            maxes.get(bucket),
            sumOfSqrs.get(bucket),
            sigma,
            format,
            metadata()
        );
    }

    @Override
    public InternalAggregation buildEmptyAggregation() {
        return new InternalExtendedStats(name, 0, 0d, Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, 0d, sigma, format, metadata());
    }

    @Override
    public void doClose() {
        Releasables.close(counts, maxes, mins, sumOfSqrs, compensationOfSqrs, sums, compensations);
    }
}
